Text Generation
Transformers
Safetensors
qwen3
reasoning
extrapolation
rl
qwen
text-generation-inference
Instructions to use HerrHruby/RCT-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HerrHruby/RCT-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HerrHruby/RCT-4B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HerrHruby/RCT-4B") model = AutoModelForCausalLM.from_pretrained("HerrHruby/RCT-4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HerrHruby/RCT-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HerrHruby/RCT-4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HerrHruby/RCT-4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HerrHruby/RCT-4B
- SGLang
How to use HerrHruby/RCT-4B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "HerrHruby/RCT-4B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HerrHruby/RCT-4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "HerrHruby/RCT-4B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HerrHruby/RCT-4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HerrHruby/RCT-4B with Docker Model Runner:
docker model run hf.co/HerrHruby/RCT-4B
Commit History
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Add model card for Reasoning Cache (RCT-4B)
Verified signatureThis PR adds a comprehensive model card for the `RCT-4B` model, which implements the Reasoning Cache (RC) algorithm. Key improvements include: - Added `pipeline_tag: text-generation` for better discoverability. - Added `library_name: transformers` to enable the automated `transformers` code snippet, as the model architecture is compatible. - Included relevant `tags` such as `reasoning`, `extrapolation`, `rl`, and `qwen`. - Provided a clear model description based on the paper abstract. - Linked to the official research paper: [Reasoning Cache: Continual Improvement Over Long Horizons via Short-Horizon RL](https://huggingface.co/papers/2602.03773). - Added a link to the official GitHub repository: [IanYHWu/rc](https://github.com/IanYHWu/rc). - Included a usage section explaining the need for RC-decoding logic, referencing vLLM for inference and verl for training, and clarifying `transformers` compatibility for loading. - Added a BibTeX citation. This enhanced model card provides essen…27bd919
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